Last updated: 2021-06-15
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Knit directory: factor_analysis/
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| /project2/xinhe/xsun/website/factor_analysis/output/simple_sum_pcs_reg_5_d1k_plt_cedar.txt | output/simple_sum_pcs_reg_5_d1k_plt_cedar.txt |
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| /project2/xinhe/xsun/website/factor_analysis/output/simple_sum_pcs_reg_5_d1k_cd14_cedar.txt | output/simple_sum_pcs_reg_5_d1k_cd14_cedar.txt |
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We used the PLIER R package with the collection of 5546 gene sets as a prior information matrix priorMat available in the package comprising canonical, immune and chemgen pathways from MSigDB (same with the eLife paper)
We used 26 immune and blood cell traits in this part, they are: “mch”,“mchc”,“mcv”,“rdw”,“ret”,“baso”,“plt”,“pct”,“pdw”,“mpv”,“hct”,“hgb”,“ret”,“mono”,“T1D”, “EUR.IBD”,“EUR.UC”,“EUR.CD”,“ukb.allasthma”,“eo”,“wbc”,“rbc”,“myeloid_wbc”,“gran”,“lymph”,“neut”,“allergy”
| cell type | LV num |
|---|---|
| platelet | 132 |
| B cell | 136 |
| T cell(CD4) | 146 |
| T cell(CD8) | 134 |
| monocyte CD14 | 88 |
| neutrophils CD15 | 146 |
For some interested LVs, we did ORA enrichment analysis.
We tried to 1) use all genes have non-zero loadings for each LV 2)sorted the genes that have non-zero loadings by their loadings, taking the top 25% as the gene set.
The function database is geneontology biological process. The reference set affy hugene 2 0 st v1. Minimum number of genes for a category is 5, maximum number of genes for a category is 2000(default settings). All categories that have fdr<0.05 are listed.
We did enrichment analysis for the genes in ‘T1D_cd15_lv41’.
Using all genes have non-zero loadings (3112 genes):
Using top 25% genes(778 genes):
No significant gene set is identified based on FDR 0.05. We just found 4 pathways have fdr < 0.2
lv96:EUR.CD,EUR.IBD
Using all genes have non-zero loadings (4458 genes):
Using top 25% genes(1114 genes):
No significant gene set is identified based on FDR 0.05.
lv80: EUR.UC
Using all genes have non-zero loadings (5293 genes):
Warning in instance$preRenderHook(instance): It seems your data is too big
for client-side DataTables. You may consider server-side processing: https://
rstudio.github.io/DT/server.html
Using top 25% genes (1323 genes):
lv49: EUR.CD
Using all genes have non-zero loadings (4978 genes):
Warning in instance$preRenderHook(instance): It seems your data is too big
for client-side DataTables. You may consider server-side processing: https://
rstudio.github.io/DT/server.html
Using top 25% genes(1244 genes):
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 rstudioapi_0.11 whisker_0.3-2 knitr_1.30
[5] magrittr_1.5 R6_2.4.1 rlang_0.4.8 highr_0.8
[9] stringr_1.4.0 tools_3.6.1 DT_0.15 xfun_0.18
[13] git2r_0.26.1 crosstalk_1.1.0.1 htmltools_0.5.0 ellipsis_0.3.1
[17] rprojroot_1.3-2 yaml_2.2.1 digest_0.6.25 tibble_3.0.3
[21] lifecycle_0.2.0 crayon_1.3.4 later_1.1.0.1 htmlwidgets_1.5.2
[25] vctrs_0.3.4 promises_1.1.1 fs_1.5.0 glue_1.4.2
[29] evaluate_0.14 rmarkdown_1.13 stringi_1.5.3 compiler_3.6.1
[33] pillar_1.4.6 backports_1.1.10 jsonlite_1.7.1 httpuv_1.5.1
[37] pkgconfig_2.0.3